Abstract
Recent advancements in the Internet of Vehicles (IoV) technology have pathed the way for the use of various smart services for the management of urban traffic, including authentication and key management. Key management protocols are an important means of addressing security and privacy concerns. However, they can be resource-intensive in terms of network traffic and workload management, particularly at times of traffic congestion, which in turn can increase the ECU and RSU processing load and adversely impact network communications. This paper introduces a more efficient key management method, named AI-enabled and Layered Key Management (ALKM), which uses an Artificial Intelligence (AI) approach and a layered workflow to reduce network traffic and workload. Specifically, the ALKM distributes dynamic synchronous time-dependent keys among Road Side Units (RSUs) rather than static cryptographic keys. It provides three layers of secure communications: public, tunnel, and hierarchy. The public layer creates a flat secure layer between the Traffic Management Center (TMC) and all RSUs. Using AI-enabled features, the tunnel layer predicts short-term and long-term congestion areas by analysis of the acquired trajectory data, and then establishes a secure communication channel between the selected RSUs and the TMC. In the hierarchy layer, in multiple tiers, the TMC and higher-level RSUs assist lower RSUs in message decryption (but not vice-versa). Our extensive analysis shows that the ALKM generates overall between 48% and 99% less network traffic per generated key than the Master Key method, depending on the operational lifetime of the keys used.